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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3561-3570 订阅

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A Hardware Prototype Targeting Distributed Deep Learning for On-device Inference

A Hardware Prototype Targeting Distributed Deep Learning for...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Allen-Jasmin Farcas Guihong Li Kartikeya Bhardwaj Radu Marculescu The University of Texas at Austin Austin TX USA Arm Inc San Jose CA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper presents a hardware prototype and a framework for a new communication-aware model compression for distributed on-device inference. Our approach relies on Knowledge Distillation (KD) and achieves orders of magnitude compression ratios on a large pre-trained teacher model. The distributed hardware prototype consists of multiple student models deployed on Raspberry-Pi 3 nodes that run Wide ResNet and VGG models on the CIFAR10 dataset for real-time image classification. We observe significant reductions in memory footprint (50×), energy consumption (14×), latency (33×) and an increase in performance (12×) without any significant accuracy loss compared to the initial teacher model. This is an important step towards deploying deep learning models for IoT applications.

关键词： computer vision pattern recognition conferences

来源：评论

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sungmin Eum David Han Gordon Briggs US Army Research Lab US Naval Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.

关键词： Visualization Image color analysis Task analysis computer vision Image resolution Training Complexity theory

来源：评论

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VoronoiNet : General Functional Approximators with Local Support

VoronoiNet : General Functional Approximators with Local Sup...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Francis Williams Jérôme Parent-Lévesque Derek Nowrouzezahrai Daniele Panozzo Kwang Moo Yi Andrea Tagliasacchi New York University McGill University University of Victoria Google Brain

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it - via a novel deep architecture - into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

关键词： Shape Three-dimensional displays Decoding Image reconstruction Training computer architecture Task analysis

来源：评论

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Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Christoph Gladisch Christian Heinzemann Martin Herrmann Matthias Woehrle Corporate Research Robert Bosch GmbH

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Testing Tools computer vision Computational modeling Analytical models Context modeling Software

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Story Completion with Explicit Modeling of Commonsense Knowledge

Story Completion with Explicit Modeling of Commonsense Knowl...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mingda Zhang Keren Ye Rebecca Hwa Adriana Kovashka Department of Computer Science University of Pittsburgh

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Growing up with bedtime tales, even children could easily tell how a story should develop; but selecting a coherent and reasonable ending for a story is still not easy for machines. To successfully choose an ending requires not only detailed analysis of the context, but also applying commonsense reasoning and basic knowledge. Previous work [8] has shown that language models trained on very large corpora could capture common sense in an implicit and hard-to-interpret way. We explore another direction and present a novel method that explicitly incorporates commonsense knowledge from a structured dataset [11], and demonstrate the potential for improving story completion.

关键词： Tensile stress Predictive models Context modeling Knowledge based systems Cognition Training Task analysis

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A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images

A Deep Physical Model for Solar Irradiance Forecasting with ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vincent Le Guen Nicolas Thome EDF R&D France CEDRIC Conservatoire National des Arts et Métiers Paris France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present a new deep learning approach for short-term solar irradiance forecasting based on fisheye images. Our architecture, based on recent works on video prediction with partial differential equations, extracts spatio-temporal features modelling cloud motion to accurately anticipate future solar irradiance. Our method obtains state-of-the-art results on video prediction and 5min-ahead irradiance forecasting against strong recent baselines, highlighting the benefits of incorporating physical knowledge in deep models for real-world physical process forecasting.

关键词： Forecasting Predictive models computer architecture Feature extraction Solar energy Machine learning Cameras

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Neurodata Lab’s approach to the Challenge on computer vision for Physiological Measurement

Neurodata Lab’s approach to the Challenge on Computer Visio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mikhail Artemyev Marina Churikova Mikhail Grinenko Olga Perepelkina Neurodata Lab LLC Miami USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within CVPR2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision. Our deep learning-based approach includes 3D spatiotemporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).

关键词： Heart rate Three-dimensional displays Kernel Videos Estimation Face Convolution

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An a-contrario Biometric Fusion Approach

An a-contrario Biometric Fusion Approach

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luis Di Martino Javier Preciozzi Rafael Grompone von Gioi Guillermo Garella Alicia Fernández Federico Lecumberry IIE Universidad de la República Uruguay Centre Borelli ENS Paris-Saclay Université Paris-Saclay France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.

关键词： Computational modeling Biological system modeling Stochastic processes pattern recognition Probability Estimation Training

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Extensions and limitations of randomized smoothing for robustness guarantees

Extensions and limitations of randomized smoothing for robus...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jamie Hayes University College London

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. It operates in a black-box and so certification is not constrained by the size of the classifier's architecture. Here, we extend the work of Li etal. [26], studying how the choice of divergence between smoothing measures affects the final robustness guarantee, and how the choice of smoothing measure itself can lead to guarantees in differing threat models. To this end, we develop a method to certify robustness against any ℓ p (p ∈ N >0 ) minimized adversarial perturbation. We then demonstrate a negative result, that randomized smoothing suffers from the curse of dimensionality; as p increases, the effective radius around an input one can certify vanishes.

关键词： Smoothing methods Robustness Perturbation methods computer architecture Random variables Visualization computer vision

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Continual Learning for Anomaly Detection in Surveillance Videos

Continual Learning for Anomaly Detection in Surveillance Vid...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Keval Doshi Yasin Yilmaz University of South Florida Tampa FL

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learning framework due to computational and storage issues. Furthermore, online decision making is an important but mostly neglected factor in this domain. Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and continual learning, which in turn significantly reduces the training complexity and provides a mechanism for continually learning from recent data without suffering from catastrophic forgetting. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.

关键词： Videos Feature extraction Anomaly detection Training Neural networks Surveillance computer vision

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